论文标题
使用固定时间序列模型的基于随机识别的主动传感声音耗散SHM
Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models
论文作者
论文摘要
在这项工作中，提出了使用随机时间序列模型的概率损伤检测和识别方案，在Acousto-ultrasound引导波的背景下，提出了其性能，并通过实验评估其性能。为了简化损伤检测和识别过程，根据单数值分解（SVD）以及主要组件分析（PCA）基于基于的截断方法对模型参数进行修改。然后，修改模型参数用于估计遵循卡方分布的统计特征数量。使用概率阈值代替用户定义的边距来促进自动损害检测。该方法的有效性是通过使用金属和复合优惠券的多个实验评估的，并在各种损坏场景下使用损坏相交和损坏非交流路径进行评估。该研究的结果证实了以潜在自动化的方式基于引导波的损伤检测和识别的随机时间序列方法的高潜力和有效性。
In this work, a probabilistic damage detection and identification scheme using stochastic time series models in the context of acousto-ultrasound guided wave-based SHM is proposed, and its performance is assessed experimentally. In order to simplify the damage detection and identification process, model parameters are modified based on the singular value decomposition (SVD) as well as the principal component analysis (PCA)-based truncation approach. The modified model parameters are then used to estimate a statistical characteristic quantity that follows a chi-squared distribution. A probabilistic threshold is used instead of a user-defined margin to facilitate automatic damage detection. The method's effectiveness is assessed via multiple experiments using both metallic and composite coupons and under various damage scenarios using damage intersecting and damage non-intersecting paths. The results of the study confirm the high potential and effectiveness of the stochastic time series methods for guided wave-based damage detection and identification in a potentially automated way.
